Real-time filtering adaptive algorithms for non-stationary noise in electrocardiograms

• Published in: Biomedical signal processing and control 2022-02, Vol.72, p.103308, Article 103308
• Main Authors: Tulyakova, Nataliya, Trofymchuk, Oleksandr
• Format: Article
• Language: English
• Subjects: ECG signal processing; EMG noise; Non-stationary noise; Real time adaptive filtering
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.103308

•A non-stationary noise with a priori unknown characteristics often contaminates ECG signals.•The proposed algorithms suppress non-stationary noise well and preserve ECG waveform and amplitude-time parameters.•Signal is processed in real time with little delay of obtaining the filter output.•The method does not process QRS-complex at a very low noise but suppresses noise with different variance well.•Re-filtering for not low noise levels is beneficial. A non-stationary noise with a previously unknown intensity level often contaminates electrocardiogram (ECG) signals. Therefore, provision of high quality suppression of the non-stationary noise in ECG is a vital task to be performed. A new lightweight adaptive method has been proposed for real-time filtering of non-stationary (from the point of view of its variance) noise in ECG with noise- and signal-dependent switching filters, appropriate for processing a local vicinity of the current input signal sample. This method does not require time for filter parameters adaptation and a priori information about the noise variance. A one- and a two-pass algorithm on the simple optimal Savitzky & Golay filters and on the linear averaging filter have been developed on the basis of the proposed method. There is also an algorithm suggested applying a re-filtering only when the identifiers used in the method define a not low noise level. The integral and local statistical estimates of filters' efficiency have been obtained from numerical simulations over mean-square error (MSE), maximum absolute error (MAE), and signal-to-noise ratio (SNR) for a model ECG signal under different levels of Gaussian noise. Filtering efficiency was estimated with the real signals taken from physionet.org database. The filter parameters were chosen by numerical simulations for a typical P-QRS-T cycle with corresponding signal sampling rate and scale considered. For a wide range from low to high noise levels (input SNR belongs to the interval from 25 to 0 dB), the statistical estimates of efficiency have been obtained as follows: for an ECG sampled at 360 Hz (taken from NSTDB), inside QRS-complex, the SNR increases by 2.5–6.7 dB, the MSE decreases in 1.7–4.3 times and the MAE decreases in 1.3–2.2 times; inside the segments prior to and following QRS-complex, the SNR, on an average, increase by 8.6–13.2 dB and the MSE decreases in 7.1–19.2 times, and the MAE decreases in 2.4–5.1 times. For an ECG sampled at 1 kHz (taken from PTB), inside QRS-co